Beirit Harvey

If you are trying to line up next generation GPUs for big training runs or high throughput inference, you are probably looking at NVIDIA’s Blackwell B200. The catch is obvious: supply is tight, pricing is steep, and you are pushed deeper into a single vendor stack.

AMD’s MI355X Is the Alternative You’ve Been Waiting For

If you are trying to line up next generation GPUs for big training runs or high throughput inference, you are probably looking at NVIDIA’s Blackwell B200. The catch is obvious: supply is tight, pricing is steep, and you are pushed deeper into a single vendor stack.

AMD’s Instinct MI355X gives you another option. It is a very real NVIDIA B200 alternative, with comparable or better specs in the places that matter for large models, plus an open software stack and growing cloud availability on plat

If you are trying to line up next generation GPUs for big training runs or high throughput inference, you are probably looking at NVIDIA’s Blackwell B200. The catch is obvious: supply is tight, pricing is steep, and you are pushed deeper into a single vendor stack.

AMD’s Instinct MI355X gives you another option. It is a very real NVIDIA B200 alternative, with comparable or better specs in the places that matter for large models, plus an open software stack and growing cloud availability on platforms like TensorWave.

Please note, the numbers listed are for sparse tensor operations. Two things jump out immediately:

 * MI355X carries 50 percent more HBM than B200 (288 GB vs 192 GB) at similar bandwidth.
 * In low precision formats used for LLMs, MI355X and B200 are in the same performance tier, with MI355X often testing a bit ahead in FP8 style metrics and clearly ahead in FP64.

That combination is exactly what you want if your bottleneck is memory capacity and parallelism rather than one more notch of raw FP4 speed.


Where MI355X Actually Pulls Ahead


1. More memory per GPU, simpler scaling

The biggest differentiator is memory capacity.

 * MI355X: 288 GB HBM3E per GPU
 * B200: 192 GB HBM3e per GPU

That extra 96 GB is not just “nice to have.” It shows up in:

 * Larger models per GPU
   You can keep a much bigger slice of a model on each device before you need heavy tensor or pipeline parallelism.
 * Bigger batches and longer contexts
   For long context LLMs or multi modal models with large image and video embeddings, memory is usually your first limit. MI355X lets you push that limit further before you have to cut context or shard more aggressively.

In other words, for many real workloads MI355X behaves like “more than one B200” simply because you have more room for parameters and activations.


2. Stronger FP64 and Balanced Mixed Precision

For pure AI, you mostly care about low precision. But many teams are also running simulation, data prep, or custom ops that lean on FP32 and FP64.

 * MI355X delivers around double the FP64 throughput of a B200 class GPU, while staying in the same ballpark for FP8 / BF16 performance.

That makes MI355X more attractive if you live at the intersection of AI + HPC, or if you just want one cluster that can handle everything from LLMs to heavy numerical workloads.


3. Open software stack and flexible networking

B200 is deeply tied to CUDA, NVLink, and NVIDIA’s vertically integrated ecosystem. That is great if you are already all in, but it also locks you into one vendor.

MI355X leans the other direction:

 * ROCm gives you an open, CUDA alternative stack that now supports several major AI frameworks (PyTorch, TensorFlow, JAX, vLLM, Triton, and more) with production grade performance.
 * The MI350 series is designed for Ethernet based scale out, not just proprietary interconnects, which lines up well with modern AI factories that want UEC ready, standards based networking instead of bespoke islands.

If “no lock in” and open networking are part of your strategy, MI355X is much closer to how you want your future clusters to look.


Why MI355X on TensorWave is a practical NVIDIA B200 alternative

Specs are only half the story. The other half is who can actually get you the hardware at scale.

TensorWave has already deployed one of the largest AMD GPU clusters in North America, with 8,192 MI325X GPUs in a liquid cooled, high density installation specifically built for large model training.

On top of that foundation, TensorWave is now rolling out MI355X capacity:

 * AMD’s own and industry coverage note that TensorWave is among the first clouds bringing MI355X online for external customers, with 288 GB HBM3E and 8 TB/s bandwidth per GPU.
 * The platform is built as an AMD only GPU cloud, so scheduling, firmware, drivers, and ROCm are tuned specifically for Instinct accelerators rather than split across two very different architectures.

For teams that are chasing B200 but fighting lead times and capacity constraints, MI355X on TensorWave gives you:

 * Comparable performance class for large LLM training and high throughput inference
 * More memory per GPU so you can simplify your parallelism strategy
 * An open stack that avoids tying your entire roadmap to one vendor
 * Actual access to large pools of GPUs instead of waiting in line for Blackwell deliveries

That combination is what makes MI355X a very real NVIDIA B200 alternative, not just on paper but in how you can run jobs this quarter.


Looking Ahead: MI455X and the Next Jump

The MI355X is not the end of AMD’s story either. AMD has already confirmed its next Instinct generation, often referred to as the MI400 / MI450 series, with projected specs like: 

 * Up to 432 GB of HBM4 per GPU
 * Around 19.6 TB/s of memory bandwidth per device
 * Roughly double the FP4 and FP8 compute compared to the MI350 series

The MI455X class parts are expected to push even further into ultra large model territory, making trillion parameter training and massive multi modal inference more routine rather than exotic.

For now, though, if you are looking for a B200 class GPU you can actually get your hands on, with more memory and an open stack, AMD’s MI355X on TensorWave is one of the strongest options in the market.


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TensorWave is the industry’s premier AMD-exclusive cloud provider, delivering memory optimized AI infrastructure for training, fine-tuning and inference. 

Our platform combines the raw computational power of AMD's latest GPU architectures with innovative liquid cooling technology and ultra-high-speed networking, creating an optimized environment for the most demanding AI workloads. From large language model training to real-time inference, TensorWave provides the performance, scalability, and cost-efficiency that modern AI initiatives demand.

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AMD Instinct™ MI355X GPUs Now Available

AMD’s MI355X Is the Alternative You’ve Been Waiting For

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